Solar flare forecasting based on swin transformer and temporal convolutional networks

Abstract

Solar flares, intense solar eruptions, discharge electromagnetic radiation and energetic particles that may have major consequences for both space weather and Earth’s atmospheric conditions. Therefore, developing high-precision forecasting models is crucial. In this paper, we propose a solar flare prediction model, which integrates the Swin Transformer with a TCN augmented by a global attention mechanism, named SwinTCN-Att, for predicting whether ≥C- and ≥M-class flare events will erupt in the solar active regions (ARs) in the next 24 hours. We collected magnetogram data from solar ARs obtained from the Space Weather Helioseismic and Magnetic Imager Active Region Patch (SHARP) dataset, spanning from May 2010 to December 2019, and selected 16 magnetic field feature parameters from the SHARP data. The construction of the model is carried out in two stages: first, the spatial characteristics of the magnetogram are captured using the Swin Transformer; next, these spatial features are integrated with 16 magnetic field parameters. Temporal features are then derived using TCN with a global attention mechanism to predict solar flares. Then, following model training and testing, we evaluated performance using five different assessment metrics, with the True Skill Statistic (TSS) serving as the primary evaluation metric. The results show that the TSS scores achieved were 0.825 ± 0.042 for ≥C-class flares and 0.879 ± 0.025 for ≥M-class flares, marking a significant improvement over previous models. These results demonstrate that the proposed SwinTCN-Att model effectively integrates relevant solar flare information, combines the strengths of both individual models, and captures solar flare evolution features, achieving superior predictive performance.